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DOI	10.1126/science.aay3062
	Crystal symmetry determination in electron diffraction using machine learning
	Kaufmann K.; Zhu C.; Rosengarten A.S.; Maryanovsky D.; Harrington T.J.; Marin E.; Vecchio K.S.
发表日期	2020
ISSN	0036-8075
起始页码	564
结束页码	568
卷号	367 期号:6477
英文摘要	Electron backscatter diffraction (EBSD) is one of the primary tools for crystal structure determination. However, this method requires human input to select potential phases for Hough-based or dictionary pattern matching and is not well suited for phase identification. Automated phase identification is the first step in making EBSD into a high-throughput technique. We used a machine learning-based approach and developed a general methodology for rapid and autonomous identification of the crystal symmetry from EBSD patterns. We evaluated our algorithm with diffraction patterns from materials outside the training set. The neural network assigned importance to the same symmetry features that a crystallographer would use for structure identification. © 2020 American Association for the Advancement of Science. All rights reserved.
关键词	algorithm artificial neural network crystal structure detection method identification method machine learning algorithm article crystal electron diffraction human machine learning
语种	英语
来源机构	Science
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/133760
推荐引用方式 GB/T 7714	Kaufmann K.,Zhu C.,Rosengarten A.S.,et al. Crystal symmetry determination in electron diffraction using machine learning[J]. Science,2020,367(6477).
APA	Kaufmann K..,Zhu C..,Rosengarten A.S..,Maryanovsky D..,Harrington T.J..,...&Vecchio K.S..(2020).Crystal symmetry determination in electron diffraction using machine learning.,367(6477).
MLA	Kaufmann K.,et al."Crystal symmetry determination in electron diffraction using machine learning".367.6477(2020).